Under privacy law, a customer asks you to delete all of their data. How should an ML platform handle this across datasets, features, indexes and models?

What the interviewer wants to hear

• Lineage and catalogues to find every copy of the data
• Delete from raw stores, feature stores, vector indexes, caches and logs
• Handle training snapshots and models through a documented policy
• Prefer data minimisation and retention limits up front
• Audit and confirm completion within the legal deadline

Model answer

The hard part is finding every copy, so the platform needs lineage and a data catalogue that link a user identifier to raw tables, derived features, embeddings in vector indexes, caches, logs and training snapshots. A deletion workflow then removes or anonymises records in each system and records proof for the audit. For online stores and vector indexes this is a direct delete. For training data, snapshots are rebuilt without the user, and for trained models we follow a documented policy agreed with legal: often the next scheduled retrain on cleaned data is enough, but high-risk cases may need retraining sooner. Prevention matters even more: keep personal data out of features and prompts where possible, pseudonymise identifiers, set retention limits on logs and snapshots, and avoid copying data into ad hoc notebooks. The workflow must finish within the legal deadline and be tested regularly.

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